Linode’s Post

Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication: "allowing LLMs to autonomously select the most suitable format before reasoning or communicating leads to a 3.3 to 5.7% improvement in reasoning efficiency for different LLMs, and up to a 72.7% reduction in token usage in multi-agent communication" Code: https://lnkd.in/eXTz83NQ Paper: https://lnkd.in/e_xJZmYb

AI-powered search systems employ advanced techniques like semantic search and question-answering based on LLMs. Read more: https://lnkd.in/dF_K-76A

Knowledge Graphs x LLMs: A collab your enterprise application didn't know it needed. Knowledge graphs don't just store structured facts. They are like the maps for language models to target accurate information for you.

I just posted a short video from IBM explaining knowledge graphs in a simple way, this video takes it one step further and combines it with LLMs… #knowledgegraphs #graphdatabase #llm

Short and to the point on why #llms need #knowledgegraphs to unlock their full potential. But the business case in knowledge graphs is so much bigger than "just" #aiagents going beyond the parrot state. Done at scale, they will open up avenues of automation, business insights and application of AI you cannot imagine while chugging along in a #rdbms world.

In this article, Marie Stephen Leo compares three open-source frameworks, Instructor, Fructose, and Langchain, to identify the best overall framework for three complex real-world structured data parsing tasks

From bad to worse: a new slimmed down Whisper model is now available open source and has the potential to be run locally. "Trained on >5M hours of labeled data, ... a finetuned version of a pruned ... exact same model, ... way faster, at the expense of a minor quality degradation." https://lnkd.in/e7ec5xPu

This paper discusses how three tasks in the FinLLM challenge: financial classification, text summarization, and single stock trading. The classification task involves identifying claims and premises in financial texts, the summarization task involves creating concise summaries of long financial documents, and the trading task involves making predictive trading decisions based on algorithmic insights. The approach taken in the article involved fine-tuning large language models (LLMs) using PEFT and LoRA. They used two specific models, Llama3-8B and Mistral-7B. These models were pre-trained on a wide range of data, making them versatile and adaptable for financial tasks. This picture shows a proposed fine-tuning method (picture is from the article). Source: https://lnkd.in/dX_GXnd7

Advancing time series analysis with multi-granularity guided diffusion model; An algorithm-system co-design for fast, scalable MoE inference; What makes a search metric successful in large-scale settings; learning to solve PDEs without simulated data. https://msft.it/6045lyVfD

Welcome to Research Focus, where we spotlight Microsoft’s trailblazing research in AI and sustainability, shaping a greener, smarter future in technology! 🌱🧠 Revolutionary Time Series Analysis: Discover MG-TSD, a cutting-edge model that uses multi-granularity guided diffusion to set new benchmarks in long-term forecasting. This innovation promises significant improvements without the need for additional data, marking a leap forward in predictive analytics. 📈🔍 Scalable AI Applications: The Pre-gated MoE architecture is making waves by addressing the high memory demands of Mixture-of-Experts models. This co-designed algorithm-system solution not only reduces GPU memory consumption but also maintains high performance, paving the way for more scalable AI applications. 💻🚀 Efficient Neural Networks: LordNet, an efficient neural network designed to solve complex partial differential equations without the need for simulated data, is also making headlines. This model is 40 times faster than traditional solvers and offers superior accuracy and efficiency, showcasing the potential of AI in scientific research. 🧠🔬 Advanced Predictive Analytics: FXAM is setting new standards in predictive analytics with its unified and fast interpretable model. By extending the capabilities of Generalized Additive Models, FXAM ensures high accuracy and training efficiency, making it a powerful tool for interactive analysis. 📊💡 Dive into these extraordinary innovations that are redefining the realms of technology and sustainability. Join in celebrating the brilliant minds behind these advancements. #MicrosoftResearch #AIForGood #TechInnovation #FutureOfAI #Sustainability